December 2022 Satiprasad Sahoo and Ajit Govind Table of Contents 1. Introduction…………………………………………………………............................................................................. ..................... 3 2. Study area and Methodology …………………………………………………………………………………………………………………………… 6 3. Analysis of the spatiotemporal trend of historical climatic conditions from TerraClimate………………………………… 8 3.1 Analysis of the spatiotemporal trend of future climatic conditions from CMIP6…………………………………….. 16 4. Analysis of the spatiotemporal trends of historical hydrological conditions from TerraClimate………………………. 19 5. Analysis of the spatiotemporal trends of water storage from GRACE………………………………………………………………. 22 5.1 Analysis of the spatiotemporal trends of current terrestrial water storage conditions from GLDAS……………………………………………………………………………………………………………………………………………………………….. 23 5.2 Analysis of the spatiotemporal trends of current groundwater storage conditions from GLDAS 25 6. Correlation Analysis…………………………………………………………………………………………………………………………………………. 26 7. Climate-resilient agriculture and development practices…………………………………………………………………………………. 28 7.1 Soil resilience………………………………………………………………………………………………………………………..................... 28 7.2 Adaptation in crop varieties…………………………………………………………………………………………………..................... 28 7.3 Water management………………………………………………………………………………………………………………………………… 28 7.4 Conservation tillage………………………………………………………………………………………………………………………………… 29 7.5 Farm equipment hiring…………..……………………………………………………………………………………………….................. 7.6 Adaptation of livestock systems.………………………………………......................................................................... 29 29 8. Recommendations………………………………………………………………………………………………………………………………………….. 29 9. Summary and Conclusions……………………………………………………………………………………………………………………………… 30 10. References……………………………………………………………………………………………………………………………………………………. 32 2 1. Introduction Climate change to greenhouse gas emissions is an indisputable fact worldwide and the average surface temperature will increase by 1.5˚C from 2030 to 2052 over the globe (Zong et al., 2022). The food security problem has a major challenge over the world. Agriculture is an economic activity and plays an important role in socio-economic development. However, Climate-Resilient Agriculture (CRA) is very important for increasing agricultural productivity under climate variabilities from the local to global scale. Agriculture income can be reduced by 15-25% due to changing climatic conditions. Thus, CRA can be reducing hunger and poverty to achieve long-term higher productivity. The CRA is one type of composite approach including the food security and components of agriculture directly affected by climate change. The World Bank promotes CRA as a “triple win” as follows: (i) enhanced productivity, (ii) resilience, and (iii) carbon sequestration. There is strong statistical evidence that the climate is changing more quickly than expected, which will harm the economic development of developing countries as well as the general health of the community (Intergovernmental Panel on Climate Change (Masson-Delmotte et al., 2018). The agricultural industry is one of several that will be adversely impacted by shifting rainfall patterns and rising temperatures (Dube et al., 2016). The effects of climate change have a significant impact on farmers since they rely so much on agricultural activity. Therefore, farmers also need to know about the effects of climate change. Information about the climate is only valuable in mitigating the dangers caused by climate change when it is accessible in a way that smallholder farmers can comprehend (Muema et al., 2018). Therefore, it is crucial to assess the major issues that prevent smallholder farmers from successfully utilizing weather information. Due to socioeconomic and political issues, a lack of adaptation ability, and several stresses of various sizes and sectors, Africa is especially susceptible to the dangers caused by climate change. The intense weather events, such as floods, droughts, and windstorms, which are brought on by climate change, have a substantial impact on food security in west Africa (Sultan & Gaetani, 2016; Sylla et al., 2018). Ethiopia is one of the biggest countries in Africa. Due to its geographic location, there is a large variation in temperature and precipitation. The movement of the Intertropical Convergence Zone (ITCZ) and associated atmospheric circulation significantly affect Ethiopia's climate. Particularly effective in controlling the local temperature are the nation's specific elevational and morphological characteristics. This explains why the local climate varies within proximity. The agricultural operations in the area are significantly impacted by this type of regional climate variation. The bulk of agricultural operations in Ethiopia include irrigation and rain-fed agriculture, and one of the most crucial water sources for irrigation is groundwater. Ethiopia is now one of the most susceptible nations to climate change and fluctuation, regularly experiencing the effects of climate-related catastrophes including floods and drought (Rovin et al., 2013). In the majority of Ethiopia, rain-fed agriculture is the primary source of growing crops. Nationally, the main causes of hunger and food scarcity are thus a lack of precipitation and the accompanying drought (Admassie et al., 2008). Precipitation is the main factor affecting agricultural activities in Ethiopia's northern region. Therefore, a significant influence on the quality of life for the inhabitants in this region is caused by a drop in rainfall (Tesfahunegn et al., 2016). Ethiopia has the lowest ability to adapt to climate change in Africa (Adem & Amsalu, 2012; Conway & Schipper, 2011; Stige et al., 2006; Thornton et al., 2010). This is a result of dependence on rain-fed agriculture, poor infrastructure of water supplies, fast population increase, a lack of ability to adaptation, shoddy organizations, and a lack of knowledge of environmental change (Abebe Tadege, 2007). Also, Ethiopian mean annual temperatures are 3 expected to rise by 0.9 to 1.1 degrees Celsius by 2030, 1.7 to 2.1 degrees Celsius by 2050, and 2.7 to 3.4 degrees Celsius by 2080, according to the International Panel on Climate Change (Oakes, 2009). In a similar vein, EPCC noted an emerging significant fluctuation in the rainfall season (2015). During the previous several years, seasonal mean temperatures have increased in nations like Ethiopia (Legesse et al., 2013). Rising temperatures and volatility in rainfall will have an impact on the agricultural sector. The region's land use has seen a substantial alteration as a result of climatic variability. Therefore, changes in land use and the climate have a significant impact on hydrological processes (Z. Li et al., 2009; Mendes & Maia, 2016; Thapa, 2021). Across all geographical and temporal dimensions, LULC change is one of the most significant contributors to changes in the land surface (Bosch & Hewlett, 1982; Conway, 2000). The change in land surface brought on by LULC has a profound influence on the hydrological processes (Costa et al., 2003; Fang et al., 2013; Gashaw et al., 2018; Guo et al., 2008; Woldesenbet et al., 2017; Worku et al., 2017; L. Zhang et al., 2001). Changes in LULC can affect terrestrial hydrology by changing the long-term equilibrium between rainfall and evapotranspiration (Garg et al., 2019). Several earlier studies have demonstrated the impacts of LULC modification on runoff, and evapotranspiration (Bosch & Hewlett, 1982; Dong et al., 2015; Fang et al., 2013; Gashaw et al., 2018; G. Li et al., 2017; Worku et al., 2017; Yang et al., 2012; Yin et al., 2017; L. Zhang et al., 2001; Y. Zhang et al., 2014). The runoff potential is significantly increased by the extension of agricultural land at the expense of plant cover d (Bosch & Hewlett, 1982; Dong et al., 2015; Fang et al., 2013; Gashaw et al., 2018; Teklay et al., 2019; Worku et al., 2017). Nevertheless, the features of the agro-ecological contexts of the research sites determine how much LULC or climatic fluctuations affect differences in runoff and evapotranspiration(Dong et al., 2015; Guo et al., 2008; G. Li et al., 2017; Ma et al., 2009). In Ethiopia's policy agenda, the supportable use of water is gaining importance, because this is one of the emerging nations where agricultural activity is the foundation of the economy, and where LULC-related natural issues pose significant problems to agriculture (Dwivedi et al., 2005). Therefore, a better understanding of how LULC influences watershed hydrology will enable local governments and policymakers to formulate and implement effective and appropriate response strategies. The impact of LULC on hydrological processes varies depending on the locale, and it is essential to look at the link between LULC and hydrological outputs to enable effective land and water management actions. Agriculture is the foundation of the economies of nations like Ethiopia, and water resources may also be decreased as a result of agricultural operations, including flow management and irrigation techniques. Thus, the agricultural industry is now being impacted by the drought, which was caused by this issue (Jaramillo & Destouni, 2015; Winkler et al., 2017). Water and food availability are significant issues that mankind is currently experiencing due to an expanding population and continuous environmental issues (Hao et al., 2014). It is crucial to research water availability so that people may determine if the demand can be met by the existing water sources (Padowski & Jawitz, 2012). In the management of water resources, the concept of water availability is strongly related to the more general idea of water sustainability (Barlow et al., 2004). Poor water management and a worsened availability of water for all users are caused by inadequate hydrological data (Maliehe & Mulungu, 2017). Water resource expansion is encouraged through appropriate management of sustainable water resources, and several methods have been developed to assess the sustainability of water resources (Maiolo & Pantusa, 2019). The availability of water has substantially changed as a result of human and climate change (Prakash et al., 2014). In the context of an environment that is transforming, it is crucial to assess how sustainably distributed water resources are. An innovative method of assessing water resources is using satellite and global hydro-meteorological data. Also, a novel method of observing the water cycle has been made possible by the advancement of remote sensing technologies (Guo et al., 2018; 4 Seyoum, 2018; Yao et al., 2019). Information on a region's hydrological studies is provided through the Global Land Data Assimilation System (GLDAS) (Rodell et al., 2004; Syed et al., 2008). Additionally, the launch of the Gravity Recovery and Climate Experiment (GRACE) mission has made it possible to quantify TWS anomalies (TWSA) by recapturing temporal fluctuations in the Earth's gravity field at annual intervals (Wahr et al., 1998). GRACE data may be used to investigate the chronological evolution of the many components of the water cycle (Chen et al., 2010; Ramillien et al., 2006). Thus, GRACE helps overcome the abovementioned limitations, and thus, it is widely used globally. GRACE was widely utilized by researchers to assess changes in groundwater, or drought (T. Li et al., 2019; Mo et al., 2016; Shamsudduha et al., 2017; Wang et al., 2011). In places of the world with a lack of data, GRACE-derived outputs provide the potential of assessing groundwater degradation (Famiglietti & Rodell, 2013; Forootan et al., 2014; Rodell et al., 2007). Through the use of GRACE data, groundwater monitoring work has been completed across Africa (Awange et al., 2014; Kenea et al., 2020; Saber et al., 2020). Water resources are affected by climate change in terms of how they are created, stored, and how quickly they are lost. To evaluate GRACE products, the study also uses rainfall and soil moisture data based on products from the Tropical Rainfall Measuring Mission (TRMM) and the Global Land Data Assimilation System (GLDAS) , respectively. Changes in climate-resilient agriculture (CRA) under changing climatic conditions raise these research questions: • rainfed crop production systems and the livelihoods of subsistence farmers highly impacted by climate variables? • climate information accessible to smallholder farmers for agriculture water management decision- making? • what opportunity exit for the agriculture system in the agro-ecological zone? • Eco-efficient agriculture techniques required for sustainable agriculture planning? • the incentives communities may have for implementing and maintaining CRA? • scientific results should be used to increase the effectiveness of climate adaptation policies? Thus, there is a scope for the development of the CRA system under climate uncertainty. CRA refers to the individual families who have independently established and developed without much more investment from external funding. It can help to improve the livelihood strategies and attitudinal orientation of good owners. These techniques can help farmers build the capacity to adopt and effectively respond to manage the long-term climate risk. It can also improve the irrigation system for agriculture production in rural development to solve food security problems. The objectives of this study are given below: 1. To analyze the spatiotemporal trend of historical climatic data at the sub-regional level by different climatic parameters. 2. To analyze the spatiotemporal trends of historical hydrological parameters for sustainable and efficient use of water resources in the era of climate uncertainty. 3. To develop a framework for terrestrial water storage and groundwater storage mapping for the variability of the dryness and wetness conditions over an area using GLDAS and GRACE data. 5 2. Study area and Methodology Ethiopia is the nation where the study was carried out (Figure 1). Ethiopia's total area is 1,100,000 square kilometers. According to estimates, the populations of Ethiopia is 117.88 million in 2021. Ethiopia is the neighboring country of Sudan. The climate of Ethiopia varies mostly with height and ranges from the warm, dry lowlands to the chilly, wet plateau. The country, which is located just north of the equator, has rather constant temperatures all year long. Ethiopia's annual temperature ranges from 10.2 °C to 24.7 °C. The most recent regional climate prediction for Africa predicts that much of Ethiopia, sections of Eritrea, and Sudan will likely suffer drier than usual conditions. In 1984, there was a famine in Ethiopia, which resulted in the hunger deaths of an estimated 1 million people. Due to its profound impact on the populations of nation like Ethiopia, the drought situation is a serious issue in Ethiopia. The economy of Ethiopia is heavily dependent on agriculture and the production of basic materials, like the majority of underdeveloped nations. The overall methodology is shown in Figure 2. Figure 1: Ethiopia Map 6 Figure 2: Overall methodological framework 7 3. Analysis of the spatiotemporal trend of historical climatic conditions from TerraClimate One of the largest nations in Africa is Ethiopia. There is a significant fluctuation in temperature and precipitation because of its geographic position. The climate of Ethiopia is greatly manipulated by the migration of the Intertropical Convergence Zone (ITCZ) and related atmospheric circulation. The country's distinctive elevational and morphological variations are particularly efficient in regulating the local temperature. This explains why there is a variance in the local climate within proximity. This kind of regional climatic variance has a considerable influence on the region's agricultural activities. Agriculture is one of the key sectors for the economic growth of Ethiopia and facing numerous challenges of climate change due to low institutional and economic capacity. Especially, floods and drought highly impact on agricultural food system to leading food insecurity and malnutrition. Thus, unable to build climate resilience for improved agriculture activities and food systems. In Ethiopia, irrigation and rain-fed agriculture account for the majority of agricultural activities, and one of the most important water sources for irrigation is groundwater. Groundwater supplies are significantly impacted by fluctuations in precipitation and temperature. This situation creates an uncomfortable environment for the farmer’s profit and causes the cost of irrigation to be limited. These problems can be addressed with climate-resilient agriculture (CRA) and climate-smart agriculture (CSA) systems. It is needed to keep the stock of modern agricultural products which keeps the present and future agriculture system intact by keeping balance with nature. In this research, the statistical analysis of the rainfall data for the study area from 1958 to 2020 has been discussed. The maximum and minimum annual rainfall have been determined to be 1029.45 mm and 585.29 mm, respectively (Table 1). Also, the average annual rainfall data for over 62 years was found to be 778.42 mm. The study area exhibits four seasons, namely: Bega the long dry period from December to February, Belg the long rainy period from March to May, Kiremt the short dry spell from June to August and Meher the short rainy period from September to November. Belg and Meher are the two times of the year when grains are grown in Ethiopia. Rainfall patterns throughout the Belg season have a significant impact on the production of grains, which mostly include corn, wheat, sorghum, barley, and teff. The mean minimum rainfall of 11.21 mm has been recorded in Bega and the mean maximum rainfall of 299.91 mm has been recorded in Kiremt. The annual average rainfall has been calculated to be 778.42 mm and the standard deviation is 81.53 mm. The average rainfall for the Belg, Kiremt, meher, and Bega seasons has been determined to be 191.44 mm, 446.97 mm, 92.43 mm, and 47.57 mm, respectively. Among the seasons, the greatest standard deviation was in the monsoon period and the lowest standard deviation was in the winter season. However, variability (coefficient of variance) has been calculated for every season and less variability has been recorded for monsoon rainfall and high variability has been recorded for winter rainfall. The ANOVA results concluded that the winter rainfall reduction had a high statistical significance (FWinter = 4.452, p<0.01). Table 1: Mann–Kendall and Sen’s test for precipitation data of Ethiopia (1958–2021) Min (mm) Max (mm) Mean (mm) SD (mm) CV (%) Correlation Annual 585.29 1029.45 778.42 81.53 10.47379 -0.091 Belg 112.02 289.92 191.44 43.97 22.96981 0.03 8 Kiremt 244.7 475.42 354.66 45.3 12.77 -0.1 Meher 118.5 318.94 184.74 43.89 23.76 0.036 Bega 11.21 95.23 47.57 21.86 45.96211 -0.26 The Sen's slope estimator, the Mann-Kendall test, and the regression model have been used to analyze the trend of rainfall data to develop the agricultural activities of the study area. Since both the slope estimator of the Sen and tau (Z) values of Kendall were negative, the annual rainfall data trend is deteriorating. For May, June, August, October, November, and December, a positive trend has been seen because Sen's slope and Kendall's tau (Z) values were both positive and the negative trend has been for January, February, March, April, July, and September (Table 2). Table 2: Monthly rainfall trend analysis results of Ethiopia (1958-2021) Jan Feb Mar April May June July Aug Sep Oct Nov Dec Kendall's -0.16 -0.13 - 0.067 -0.065 0.152 0.03 -0.153 0.107 - 0.043 0.115 0.065 0.06 tau P-Value 0.06 0.11 0.4407 0.4406 0.07 0.73 0.07 0.21 0.61 0.18 0.45 0.48 Sen's -0.11 -0.16 -0.09 -0.12 0.36 0.05 -0.28 0.16 -0.07 0.22 0.08 0.05 Slope The linear regression analysis has been done for the annual precipitation data (Figure 3). The slope values show the trend rate of annual rainfall that is either increasing or decreasing. An increasing annual rainfall trend has been highlighted in the eastern part of Ethiopia and a decreasing annual rainfall trend has been observed in the western part of Ethiopia. In the northern portion of the study area, there is also evidence of an increasing trend in annual rainfall. 9 Figure 3: Spatial distribution of the precipitation, maximum and minimum temperature trend (slope of the linear regression) map for 1958-2020 in the study of Ethiopia. The western part of the study area has the highest temperature trend, which diminishes as one moves eastward and the minimum temperature trend has been found in the western part of the study area (Figure 3). The results of the standardized precipitation anomaly index (SPAI) of Ethiopia have been shown in Table 3 and Figure 4. In 1960, the SPAI index varies from +0.87 to -1.57. Mild wetness has been designated as a condition in various portions of the northern, western, and southern parts of Ethiopia, and the central and northern parts of Ethiopia are in a dry condition. In 1970, the SPAI index varies from +2.30 to -1.27. Most of Ethiopia's territory is in a dry condition, except for the estern part, where there has been some mild wetness. In 1980, the SPAI index varies from +1.98 to -2.10. Except for some areas in the north, every region of Ethiopia has experienced great dryness during this time. In 1990, the SPAI index varies from +1.48 to -2.45. 10 Table 3: Standardized precipitation index and their categories SPAI Category ≥2 Extremely wet 1.5 - 1.99 Severely wet 1 - 1.49 Moderately wet 0 - 0.99 Mild wetness 0 to −0.99 Mild dryness −1 to −1.49 Moderately dry −1.5 to −1.99 Severely dry ≤−2 Extremely dry The middle part, southern part, and some eastern parts of the study area were in moderate wet conditions. But the northern part faced extremely dry conditions during this period. In 2000, the SPAI index varies from +3.05 to -1.88. The southern region of Ethiopia has had great dryness throughout this time, whereas the northern region of the study area has seen extreme wetness. In 2010, the SPAI index varies from +2.31 to -2.46. The middle part of Ethiopia has been designated as extremely dry condition and extremely wet condition has been found in the western part of Ethiopia. In 2020, the SPAI index varies from +6.88 to -2.86. Excessive dryness has had a substantial impact on the majority portion of Ethiopia. This is consistent with the results (NMA, 2007), which stated that Ethiopia's frequency of dry years has increased. Farmers' capacity to adapt to climate change and unpredictability would suffer from such annual variable issues with precipitation. Similarly, the IPCC (2014) reported that in the next few decades, due to global warming, drought, flooding, and precipitation unpredictability, there will be a danger of food poverty and the collapse of food systems. The country's impoverished inhabitants are put in an uncomfortable situation by it. The agricultural industry of Ethiopia will experience increased vulnerability due to the drought issue. Changes in rainfall patterns have caused several dilemmas for farmers. Certain crops have been cultivated in a specific season and within a particular rainfall range. As rain is the primary source of agriculture in Ethiopia, the rate of production of seasonal crops is decreasing day by day due to changes in rainfall patterns and changes in rainfall amount. Sometimes the availability and variability of rainfall have an impact on families' capacity to take on risk and manage their liquidity. 11 12 Figure 4: Standardized precipitation anomaly index maps for 1960, 1970, 1980, 1990, 2000, 2010, and 2020 in the study of Ethiopia. There is also a link between rainfall patterns and the use of fertilizer. The availability of water and the frequency and quantity of fertilizer application are both influenced by the volume of rainfall. On the other hand, rainfall variability is to blame for raising the danger associated with fertilizer application because applying fertilizer under dry conditions might cause seedlings to burn and can elevate the chance that a crop will perish. The influence of temperature on agricultural productivity is significant. Plant productivity will be harmed by the predicted warming of the environment and the possibility of more drastic temperature occurrences. Farmers sometimes alter the seasonal crops they grow because of temperature changes. The temperature variation leads to changes in farmers' actions and choices, which have an impact on the exploitation of groundwater. Changes in weather and climate may have an oblique influence on groundwater collection by altering how arable land is used and how farming is conducted, which in turn affects how much water is available. Furthermore, higher temperatures may have a direct effect on how much water is available for agriculture. Hence, temperature variation has a crucial influence on the demand for water and irrigation equipment. The Standardized Anomaly index for minimum and maximum temperature have been further subjected to trend analysis (Figure 5 and Figure 6). 13 Figure 5: Standardized max. temperature anomaly index maps for 1960, 1970, 1980, 1990, 2000, 2010, and 2020 in the study of Ethiopia. 14 15 Figure 6: Standardized min. temperature anomaly index maps for 1960, 1970, 1980, 1990, 2000, 2010, and 2020 in the study of Ethiopia. 3.1 Analysis of the spatiotemporal trend of future climatic conditions from CMIP6 Individual models can aid in a better understanding of variability among predicted climates, whereas multi-model ensembles provide the range and proportion of the most likely projected outcomes of change in the climate system for a chosen CMIP6 (EC Earth3) SSP2-4.5. The spatial distribution of the future precipitation, maximum, and minimum temperature trend has been calculated by the slope of the linear regression technique from 2021 to 2100 (Figure 8). The results show that the northwestern part has an increasing precipitation trend and the northeastern and southeastern part has increasing maximum and minimum temperature trend. It was also analysis future trend of climatic conditions in some important locations in this country. The average minimum and maximum annual temperatures in Tigray were found to be 16.62 °C and 31.01 °C, respectively. Between 2040 and 2059, the Tigray's annual mean minimum and maximum temperatures were calculated to be 17.14 °C and 31.19 °C, respectively. The average minimum and maximum temperatures for the Tigray were calculated to be 17.83 °C and 31.81 °C, respectively, between 2060 and 2079. In the years 2080–2099, the Tigray's annual mean minimum and maximum temperatures were found to be 18.27 °C and 32.28 °C, respectively. It was discovered that the Tigray's average annual precipitation was 1101.92 mm from 2020 to 2039, 1230.31 mm from 2040 to 2059, 1229.29 mm from 2060 to 2079, and 1260.84 mm from 2080 to 2099. 13.98 °C and 28.18 °C, respectively, are listed as the minimum and maximum temperatures in Amhara each year. The recorded average minimum and maximum temperatures for Amhara between 2040 and 2059 were 14.48 °C and 28.43 °C, respectively. Amhara's average annual minimum and maximum temperatures were estimated to be 15.18 °C and 29.08 °C, respectively, between 2060 and 2079. The predicted average minimum and maximum temperatures for 16 Figure 7: Spatial distribution of the future precipitation, maximum and minimum temperature trend (slope of the linear regression) map for 2021-2100 in the study of Ethiopia. Amhara from 2080 to 2099 were 15.67 °C and 29.49 °C, respectively. Amhara received 1363.32 mm of precipitation annually from 2020 to 2039, 1478.07 mm from 2040 to 2059, 1452.34 mm from 2060 to 2079, and 1260.84 mm from 2080 to 2099. The annual minimum and maximum temperatures in Benishangul Gumu were measured to be 18.69 °C and 32.11 °C, respectively. It was found that the annual minimum and maximum temperatures in Benishangul Gumu were 19.07 °C and 32.13 °C, respectively, between 2040 and 2059. Between 2060 and 2079, the annual minimum and maximum temperatures in Benishangul Gumu were found to be 19.72 °C and 32.70 °C, respectively. Benishangul Gumu's annual minimum and maximum temperatures were reported to be 20.24 °C and 32.97 °C, respectively, during the years 2080–2099. The annual mean precipitation at Al Benishangul Gumu was derived as follows: 1232.68 mm from 2020 to 2039; 1422.71 mm from 2040 to 2059; 1414.31 mm from 2060 to 2079; and 1509.49 mm from 2080 to 2099. The mean minimum and maximum temperatures for the Oromia have been calculated to be 14.99 °C and 28.48 °C, respectively. From 2040 to 2059, the annual mean minimum and maximum temperatures in Oromia will be 15.47 °C and 28.66 °C, respectively. The annual mean minimum and maximum temperatures in Oromia from 2060 to 2079 have been projected to be 16.12 °C and 29.29 °C, respectively. The annual mean minimum and maximum temperatures in the Oromia are forecasted to be 16.69 °C and 29.69 °C, respectively, from 2080 to 2099. Investigations have demonstrated that the Oromia region received 974.97 mm of mean precipitation from 2020 to 2039, 1048.42 mm from 2040 to 2059, 1089.64 mm from 2060 to 2079, and 1139.96 mm from 2080 to 2099. The Gambela's mean minimum and maximum temperatures for the years 2020 to 2039 were 17 determined to be 22.30 °C and 36.11 °C, respectively. The Gambela receives 798.30 mm of precipitation on average per year. It has been determined that from 2040 to 2059, the Gambela's mean annual minimum and maximum temperatures would be 22.40 °C and 35.64 °C, respectively. It has been calculated that from 2060 to 2079, the Gambela's mean annual minimum and maximum temperatures were 22.88 °C and 35.98 °C, respectively. Between 2080 and 2099, researchers discovered that the Gambela's mean annual minimum and maximum temperatures were 23.40 °C and 36.27 °C, respectively. The annual mean minimum and maximum temperatures for SNNPR were found to be 16.92 °C and 31.29 °C, respectively, from 2020 to 2039. Between 2040 and 2059, SNNPR had yearly mean temperatures of 17.18 °C for the minimum and 31.19 °C for the highest. Between 2060 and 2079, SNNPR's annual mean minimum and maximum temperatures were found to be 17.81 °C and 31.72 °C, respectively. Between 2080 and 2099, SNNPR's annual mean minimum and maximum temperatures were predicted to be 18.31 °C and 32.04 °C, respectively. According to reports, the SNNPR saw annual mean precipitation totals of 733.43 mm from 2020 to 2039, 903 mm from 2040 to 2059, 898.69 mm from 2060 to 2079, and 959.05 mm from 2080 to 2099. It has been revealed that the Somali's annual minimum and maximum temperatures are 20.47 °C and 33.36 °C, respectively, from 2020 to 2039, The annual mean minimum and maximum temperatures of the Somali were determined to be 21.15 °C and 33.76 °C, respectively, from 2040 to 2059; 21.81 °C and 34.42 °C, respectively, from 2060 to 2079; and 22.32 °C and 34.84 °C, respectively, from 2080 to 2099. The Somali's annual mean precipitation is 419.65 mm from 2020 to 2039, 479.35 mm from 2040 to 2059, 457.73 mm from 2060 to 2079, and 475.44 mm from 2080 to 2099. It has been shown to have an annual mean temperature range between 18.73 °C and 32.28 °C from 2020 to 2039, 19.34 °C and 32.56 °C from 2040 to 2059, 20.04 °C and 32.29 °C from 2060 to 2079, and a range between 20.47 °C and 33.70 °C from 2080 to 2099 in Afar region. The annual mean precipitation in the Afar was reported to have been 464.52 mm from 2020 to 2039, 582.89 mm from 2040 to 2059, 554.09 mm from 2060 to 2079, and 612.01 mm from 2080 to 2099. In Afar and Amhara, the mean maximum and mean minimum temperatures throughout the summer in between 2020-2039 have been reported to be 36.27 °C and 14.19 °C, respectively. In Gambela and Amhara, the mean maximum and mean minimum temperatures throughout the winter have been reported to be 37 °C and 12.80 °C, respectively. In Afar and Amhara, the mean maximum and mean minimum temperatures throughout the summer in between 2040-2059 have been reported to be 36.40 °C and 14.76 °C, respectively. In Gambela and Amhara, the mean maximum and mean minimum temperatures throughout the winter have been reported to be 36.80 °C and 13.46 °C, respectively. In Afar and Amhara, in the period of 2060 to 2079, the mean maximum and mean minimum temperatures throughout the summer have been reported to be 36.91 °C and 15.36 °C, respectively. In Gambela and Amhara, the mean maximum and mean minimum temperatures throughout the winter have been reported to be 37.10 °C and 14.20 °C, respectively. In Afar and Amhara, in the period of 2080 to 2099, the mean maximum and mean minimum temperatures throughout the summer have been reported to be 37.03 °C and 15.82 °C, respectively. In Gambela and Amhara, the mean maximum and mean minimum temperatures throughout the winter have been reported to be 37.96 °C and 14.81 °C, respectively. However, climate variability directly impacts the vulnerability of the livestock industry in Southeastern Ethiopia. The higher number of cattle and sheep death was shown due to the increasing pattern of temperature and the incidence and distribution of livestock diseases also increased. Thus, climate change is highly affecting livestock production through ecosystem services. Therefore, climate changes highly impact plant and animal species to respond either poleward or upslope. Thus, the grassland distribution is very important to control environmental factors because highly responds to temperature, precipitation, and grazing pressure. The results show that poor grassland conditions 18 are shown in the central plateau of Ethiopia due to high livestock density and adverse physical conditions. Sometimes montane grassland has changed to evergreen thicket and scrub due to the flat ground meets a steep slope. Moreover, drought represents a significant constraint to crop cultivation. It is occurring when lower production of essential crops and lower incomes of farmers. This country is a major climate risk for crop cultivation because heat stress will increase due to minimum temperature in the Belg season and high maximum and minimum temperature in the Bega season. Thus, a large amount of groundwater is used for irrigation purposes. Groundwater management is a challenging task worldwide, especially in African countries because of the growing water demand for irrigation, and domestic, industrial, and ecosystem restoration. The aquifer storage and recovery (ASR) is a valuable tool for sustainable water supply depending on site-specific hydrogeological conditions. ASR projects are more applicable in areas that have high population density, changes in agriculture systems, massive dependence and increasing demand on groundwater for irrigation and domestic needs, and limited ground or surface water availability. 4. Analysis of the spatiotemporal trends of historical hydrological conditions from TerraClimate The spatiotemporal trend of hydrological parameters has been calculated by the slope of the linear regression technique from the TerraClimate dataset for the year 1958 to 2020. The various hydrological parameters like actual evapotranspiration (AET), potential evapotranspiration (PET), runoff (Q), climate water deficit (DEF), and vapor pressure deficit (VPD) were considered for trend analysis purposes. However, the loss of water in a vapor state to the atmosphere from both the earth's surface and plants is known as evapotranspiration. A cropped soil's evapotranspiration is influenced by temperature, precipitation, and the amount of moisture that the soil can retain. This is the rationale for the introduction of the idea of actual evapotranspiration. An essential element of the hydrological cycle is actual evapotranspiration and one of the most important physical processes in natural ecosystems. It describes how water and energy are transferred between the soil, land surface, and atmosphere. The idea of actual evapotranspiration may be used to elucidate how worldwide climate change happens. So, the variability of climate change has a great impact on actual evapotranspiration. The most damaged ecosystems are those in Africa, which worsens the region's already acute water deficit. One of the largest nations in Africa is Ethiopia. There is a significant fluctuation in temperature and precipitation because of its geographic position. The climate of Ethiopia is greatly manipulated by the migration of the Intertropical Convergence Zone (ITCZ) and related atmospheric circulation. The country's distinctive elevational and morphological variations are particularly efficient in regulating the local temperature. This explains why there is a variance in the local climate within proximity. This kind of regional climatic variance has a considerable influence on the region's hydrological processes. This kind of geographical condition will have an impact on actual evapotranspiration. So, an essential option for agricultural or hydrological investigations is the estimate of actual evapotranspiration. The hydrological cycle's term for the potential evapotranspiration is also crucial for appropriate water management and efficient irrigation planning, which in turn has a big impact on crop water needs and water distribution. Potential evapotranspiration may be used for a variety of purposes, such as agricultural planning, drought monitoring, and assessing the impacts of global warming. Under specific climatic circumstances, potential evapotranspiration may be 19 thought of as the highest rate of evapotranspiration that can occur while soils or trees do not have a water shortage. It's important to comprehend both the actual and potential evapotranspiration to comprehend the crop's properties during the growing period. The eastward increase in annual actual evapotranspiration (AET) from -11.47 mm/yr in the west to -o.41 mm/yr in the east resembled the pattern of precipitation (Figure 8). The highest AET was recorded in the eastern part (around -0.41 mm) and lowest in the western part (around -11.47 mm), while moderate AET showed in the central part of the area. Overall AET estimation the area has been belonging to a negative trend period from 1958– 2020. The maximum trend of annual actual evapotranspiration has been found in the eastern part of Ethiopia and the minimum and moderate trend of annual actual evapotranspiration has been found in the western and middle parts of Ethiopia. It was perceived that a relatively high potential evapotranspiration (PET) (5.53 mm) had appeared in a small portion of the northwestern and southeastern parts. The negative PET (around -0.14 mm) trend represents the north to the southern part. Moderately PET is denoted in the northeastern and southern parts of the area. Vapor pressure deficit (VPD) values vary from -0.003 to 0.09 mm. The large portion concentrated with high VPD in the central region while the small portion of the negative VPD trend showed along the western and northeastern part of the area. Moreover, high and low Q (runoff) values represent 3.25 mm and -5.53 mm in the study area. Highly Q trends results also showed a small fraction in the upper and lower part of the region. Conversely, the moderate to low Q trend is found in the western region. The spatial directional Q trends showed from the eastern zone (moderate) to the western zone (low). Climate water deficit (DEF) values vary from −0.63 to 6.31 mm. The majority of the region is characterized by the positive trend of the DEF. It is observed that the high DEF condition is visible in the whole area except the edges of the southwestern and southern parts of the area which are categorized by the low DEF (-0.63 mm). Because erosion modifies the terrain, the runoff is crucial for research on how rivers evolve. Climate and biophysical factors have a big impact on runoff. Ethiopia is a tropical nation with a predominantly hilly terrain that is situated in the Horn of Africa. The rate of deforestation is considerable, and the mechanism for managing land use along the rugged terrain is ineffective. Land clearing has increased the quantity of runoff that occurs over the surface. The main cause of soil erosion is runoff. Nowadays, the primary financial and ecological concern for all of the world's regions is soil degradation. In a country like Ethiopia, this type of soil erosion activity is becoming more prevalent every day. As a result, reduced crop output was caused by the degradation of soil conditions. Additionally, the research area's eastern, northern, and southern halves, respectively, have shown the highest annual runoff trend. As a result, these areas are seeing significant annual precipitation trends. The western and middle portion of the study area has the lowest and moderate annual runoff trend, which suggests that annual precipitation trends are modest. Additionally, the northeastern region as well as a small fraction of the southern portion has the highest trend of yearly potential evapotranspiration and a low trend in annual potential evapotranspiration has been identified for the center of Ethiopia. Some studies have proved the coherent relationship between potential evapotranspiration and actual evapotranspiration which means It was anticipated that potential evapotranspiration would rise, pushing actual evapotranspiration to rise in tandem. As a result, it is anticipated that both changes will be synchronized. 20 Figure 8: Spatial distribution of the AET, PET, Q, VPD and DEF trend (slope of the linear regression) map for 1958-2020 in the study of Ethiopia. Given the shifting weather circumstances, the climate water deficit data shows the places that are under moisture stress. Water availability and need both have a role in the development of a plant water deficit. Rising temperatures can hasten the effects of water stress by creating a water shortage in the soil and atmosphere. Also, water stress in plants could be ameliorated by increasing the temperature. As a result of the shortage of water, plants are forced to deal with several diseases. Climate variability is frequently blamed for changes in forest cover, with periods of exceptionally warm and dry weather leading to decreased yearly leaf development. Temperature and rainfall readings 21 are helpful determinants of climate change in any particular period. However, climatic water deficit helps to elucidate the climatic restriction on plant development, and this study may conclude the region's climatic conditions at a micro level. Climate is characterized as the interplay of water and energy, and the climatic water deficit, which is compounded as potential evapotranspiration minus actual evapotranspiration, monitors this relationship. When the amount of soil moisture accessible to plants to meet their evaporative needs is insufficient, this is indicated by high climatic water deficit values. Most of the regions of Ethiopia have a low trend of yearly climatic water deficits (Figure 8). In contrast, there has been a strong trend of climatic water deficit in various sections of the northeast and south. Modest annual climatic water deficits have been found in the northwest, northeast, and some southern part of the study area. Another factor that is important to plants is the vapor pressure deficit, which depends on temperature and relative humidity. Instead of relative humidity, vapor pressure deficit is a more precise approach to describe what causes a leaf to lose water. A high vapor pressure deficit means the air can hold a huge amount of water. Thus, high vapor pressure deficit levels signify times of stomata. The air is almost saturated when the vapor pressure deficit is low and therefore, the plants are unable to transpire properly. The northeast and north-west regions of Ethiopia have a significant trend of yearly vapor pressure deficit and the plants can appropriately transpire in these areas (Figure 8). Ethiopia's center, eastern, and southern regions have had a low annual vapor pressure deficit trend. 5. Analysis of the spatiotemporal trends of current water storage conditions from GRACE The heat map of the monthly EWT described the temporal variation in the GRACE data of the CSR and JPL for earth water storage mechanism at various study periods (2002-2021) in Ethiopia (Figure 9). The presented heat map has two axes where the x-axis represents a yearly distribution of EWT and the y-axis is defined by the monthly distribution of EWT. The heat map images represent the varying EWT recorded over an area during a period with different color intensities. The deep red color specified high positive EWT that indicates the months when water availability is higher than the mean for the study period and vice versa for the blue. Both solutions CSR and JPL show almost similar patterns in the seasonal variability of EWT. The EWT range of both CSR and JPL products had -15 to 40 cm. In the early years, the water availability was below the mean level from January to June and above the mean level from July to December in the Ethiopia region. In recent years, the EWT gradually improved in this zone. For example, it was generally above the mean level in July to December during 2002–2018, similar to July–December in recent years (2019–2021) for the CSR data while the JPL data had above the mean level in January–December in recent years (2019–2021). In addition, the lower mean level in January–June 2002–2018 for the CSR while the JPL found in 2002–2017. From these results, water has become more available in recent years in the region. Though the outcomes of the two products were not consistent for this zone, both GRACE products exhibited an increase in red color in recent years, indicating a better EWT. For instance, the dynamics of the EWT maximum (August-September~2020, 40cm) and minimum (March~2004, -15cm) recorded for the JPL data similar to CSR retrieved maximum EWT had 40cm in September- November 2020, and minimum recorded at -15cm in March - April during 2004 and 2005 respectively. Clearcut observation from the heat maps noticed that the historical years had low availability water during the monthly periods of January to June (2002-2018) whereas the recent years have an 22 increasing trend of EWT during July to December (2002-2021) instead of more concentrated in 2020 and 2021 (August-October). Figure 9: Monthly Equivalent Water Thickness (EWT) variation from 2002 to 2021 obtained by GRACE [Center for Space Research (CSR) and Jet Propulsion Laboratory (JPL)] datasets in Ethiopia 5.1 Analysis of the spatiotemporal trends of current terrestrial water storage conditions from GLDAS The highest and lowest TWS occurred in the year 2020 and 2017 (2338.55 mm and 319.01 mm) estimated by GLDAS 2.2 model respectively (Figure 10). The interannual TWS range value is found to be highest d (1974.7 mm) and lowest (1881.3 mm) during 2016 and 2021, respectively. The central part of the area is concentrated with the medium TWS. The lowest TWS value was observed in the eastern part of the region as well as the high TWS measured in the western part all over the years. However, the TWS level also attained decreasing trend all over the study region from the western to the eastern part of the entire area. During the current period (2015-2021) the TWS was 2019.54 mm. However, it was observed that the minimum TWS found increasing trends, during (2015-2017), then it was decreasing in the current years from 2018 to 2021. 23 24 Figure 10: Annual terrestrial water storage (TWS) maps from GLDAS 2 CLM (2015-2021) in Ethiopia 5.2 Analysis of the spatiotemporal trends of current groundwater storage conditions from GLDAS The annual GWS variability is relatively maximum during 2020 (1952.02 mm), and minimum during 2018 (225.66 mm) (Fig.). However, the range of the GWS (1726.36 mm) was recorded the time during 2015-2021 respectively (Figure 11). The inter-annual range of TWS variability is highest in 2016 (1690.333 mm), followed by 2015 (1679.67 mm), 2019 (1679.33 mm), 2017 (1677.35 mm), 2021 (1657.43 mm) and 2018 (1650.57 mm) respectively. The analysis shows that the variability of GWS over the study domain is high in the western part while low in the eastern part within the study period. Interestingly, the minimum TWS found increasing trends, during most of the study period except for 2015 and 2017. 25 Figure 11: Annual groundwater storage (GWS) maps from GLDAS 2 CLM ( 2015-2020) in Ethiopia. 6. Correlation Analysis Overall, the TWS presents strong negative correlations with the AET (-0.71), Q (-0.61), and VPD (-0.41) parameters, similarly, GWS showed strong negative correlations with the AET (-0.68), Q (-0.64) and VPD (-0.4) parameters, as well as an inverse relationship with the DEF (0.0012) indices only (Figure 12). The DEF presents a strong positive correlation with the PET (0.83) and VPD (0.67). The PPT shows a moderate negative correlation with the DEF (-0.45), Tmax (-0.52), Tmin (-0.44), and PET (- 0.57) while the positive correlation with Q (0.55). Therefore, the correlation between TWS and GWS is robust (around 0.99), which is also evident from the analysis of spatial features. 26 Figure 12: Pearson correlation map of 2020 in Ethiopia. Climate change represents a complex and interconnected risk and resilience is defined as the capacity to recover quickly from difficulties. It is required the following elements: flexibility is a systematic level to respond to each situation and can provide an improved adaptation investment strategy. Flexible regulations like renewable portfolio standards, low carbon fuel standards, and incorporating flexibility mechanisms are very significant to mitigate climate change. It is very important for comprehensiveness, detail orientation, and quick decision-making. It helps to design climate services that are better tailored for climate change responses in particular contexts. It can be understood from several perspectives like the restorative benefits of natural environments and the effectiveness of environmental education programs. It is one of the important characteristics of resilience with a potential for various applications in a social- ecological system. It is the intentional duplication of system components and response pathways to allow for partial failure within a system. Partial functional redundancy is a key expression of resilience in response to water insecurity. These approaches are very important for planning, execution, and recovery. It is the partnership that results when government, non-profit, private, and public organizations solve a problem that affects the whole community. It is one 27 type of organizational change that will be more successful when efforts are made to help people within an organization. Climate resilient agriculture (CRA) mainly depends on changing climatic conditions. Climate change can disrupt food availability and quality. The different climatic conditions like changes in precipitation pattern increase in temperatures and changes in extreme weather events highly impact agriculture productivity. Climate-resilient technologies can be faced various constraints like limited knowledge of climate-resilient adaptation measures, inadequate number of extension functions at the grass-root level, inadequate weather-based farm advisories, lack of knowledge about climate change, lack of reporting system, inadequate number of automatic weather stations by respondents in adapting climate change. Thus, CRA is a very challenging issue that can withstand the shocks of climate change and extreme weather events. CRA practices must be flexible to tackle long-term climate change and also short-term weather events. The various cultural practices like proper preparatory cultivation, clean cultivation, adjusting planting/sowing/harvesting to avoid certain pests, balanced use of fertilizers, flooding the field, draining the fields, alleyways, and harvesting of the crop are highly maintained through modern techniques. It can be reducing the cost of cultivation and energy consumption to sustain productivity. Therefore, these kinds of techniques are considered realistic solutions to the above-mentioned issues. 7. Climate-resilient agriculture and development practices Adaptation in agriculture and development practices are essential for agri-production resilience to climate change. Proper management and implementation of practice are required in an increase in agri-produce in unfavorable conditions to adapt to climate change. Some practices are given below followed at the rural level in Ethiopia: 7.1 Soil resilience Soil is the nexus of water, energy, and food and can be used for sustainable soil management. It can be calculated from the response diversity by multi-omic markers. Some essential factors like soil carbon, reducing erosion, and increasing the water retention capacity of the soil are required to improve resilience. 7.2 Adaptation in crop varieties Adaptation in crop varieties refers to the relationship between environmental factors and the growth response of crop plants. It can help to reduce the negative impact of climate change on the agriculture system to ensure stable agriculture production and introducing new crops leads to diversification of producing agriculture. A few adaptations are required to improve crop varieties as follows: stakeholder participation, success and limiting factors, costs and benefits, legal aspects, implementation time, and lifetime. 7.3 Water management Water management is one of the major priorities in agriculture policy to prevent drought and build climate-resilient agriculture. Agriculture is the largest source of livelihood for people in Ethiopia and gross domestic product (GDP) has been declining by the deficit rainfall affecting crop production and farmers' incomes. Thus, water management strategies are required to improve drought mitigation, climate resilience, rainwater harvesting, and soil moisture management. It is also focused on canal irrigation, water use efficiency, and strategies for climate-resilient agriculture. 28 7.4 Conservation tillage Conservation tillage is a tillage system that involves the planting, growing, and harvesting of crops. It is very important for cover cropping, crop rotations, composting, and soil erosion control. It is often suggested as a resource-conserving alternative to increase crop productivity without compromising the soil health and cereal cropping system. The various conservation tillage methods like zero-till, strip-till, ridge-till, and mulch till can be used for soil cultivation that leaves the previous year’s crop residue. 7.5 Farm equipment hiring The low level of agriculture mechanization is very difficult of managing small and marginal farms. Thus, modern farm equipment and technologies are essential to speed up plantation/sowing and to deal with adverse events like erratic rainfall patterns. 7.6 Adaptation of livestock systems The livestock system is very significant for the livelihood of two-thirds of rural communities. An adaptation of livestock systems is needed like a water reservoir, investing in heat-tolerant breeds, rotational grazing, and reduction in overgrazing to enhance adaptation to heat stress and degradation. However, an integrated crop-livestock system can be productive and sustainable for the climate-resilient agriculture system 8. Recommendations Climate-Resilient Agriculture (CRA) is very significant for food security under changing climatic conditions. It can be helpful for the sustainable development of rural societies. The following recommendations should be advanced to ensure CRA for Ethiopia: • Adopting an actual definition of climate-resilient agriculture. • Adaptation of appropriate mitigation technologies and agro-advisories for timely crop monitoring • Crop insurance can be used as one of the strategies for CRA. • Established local authorities for CRA. • Water smart technologies (furrow-irrigated raised bed, micro-irrigation, rainwater harvesting structure, cover-crop method, greenhouse, laser land leveling, reuse wastewater, deficit irrigation, and drainage management) can help farmers to increase agriculture production under climate uncertainty. • Adopted a good understanding of conjunctive use and artificial recharge are closely related to water resource management practices. • To make the best use of surface water from wet periods and groundwater from dry periods for conjunctive use. • Improved quality of recharge water from multiple sources. • Identification of potential mineral precipitation to avoid by adjusting pH and other properties of the recharge water. • Improved agriculture water data availability by open-source web service portal. 29 • Providing financial support for CRA projects. 9. Summary and Conclusions In this study, the objectives have been completed to understand the long-term spatiotemporal trend for climatic (1958-2100), hydrologic (1958-2020), and water storage (2002-2021) changes from TerraClimate, CMIP6, GRACE, and GLDAS datasets. The spatial distribution of the standardized anomaly index (SAI) and slope of the linear regression techniques has been performed for climatic and hydrologic trend analysis purposes. CMIP6 (EC-Earth3) SSP 2 4.5 were utilized for future climate trend analysis. The GRACE of CSR and JPL and GLDAS 2 CLM data were used for EWT, TWS, and GWS analysis purposes. The correlation analysis has been performed for hydrometeorological parameters to identify the relationship between the actual conditions. The major findings are given below: • It was observed that the average annual rainfall data for over 62 (1958-2020) years was found to be 778.42 mm and the standard deviation is 81.53 mm. • The average rainfall for the Belg, Kiremt, Meher, and Bega seasons has been determined to be 191.44 mm, 446.97 mm, 92.43 mm, and 47.57 mm, respectively. • It was notified that less variability has been recorded for monsoon rainfall and high variability has been recorded for winter rainfall. • The results show that the western part of the study area has the highest temperature trend, which diminishes as one moves eastward and the minimum temperature trend has been found in the western part of the study area. • It was found that the northwestern part has an increasing future precipitation trend and the northeastern and southeastern part has an increasing future maximum and minimum temperature trend. • In Afar and Amhara, the mean maximum and minimum temperatures throughout the summer in between 2020-2039 have been reported to be 36.27 °C and 14.19 °C, respectively. • An increasing annual rainfall trend has been highlighted in the eastern part and a decreasing annual rainfall trend has been observed in the western part of Ethiopia. • The maximum trend of annual actual evapotranspiration has been found in the eastern part of the study area. • The large portion concentrated with high vapor pressure deficit in the central region and high runoff trends results showed a small fraction in the upper and lower part of the region. • It was observed that the high climate water deficit (DEF) condition is visible in the whole area except the edges of the southwestern and southern parts of the area which are categorized by the low DEF (-0.63 mm). • Most of Ethiopia's territory is in a dry condition, except for the eastern part, where there has been some mild wetness. • It was found that the equivalent water thickness (EWT) range of both CSR and JPL products had -15 to 40 cm. 30 • It was noticed that the historical years had low availability water during the monthly periods of January to June (2002-2018) whereas the recent years have an increasing trend of EWT during July to December (2002-2021) instead of more concentrated in 2020 and 2021 (August-October). • The interannual terrestrial water storage (TWS) range value is found to be highest d (1974.7 mm) and lowest (1881.3 mm) during 2016 and 2021. • The lowest TWS value was observed in the eastern part of the region as well as the high TWS measured in the western part. • The annual groundwater storage (GWS) variability is relatively maximum during 2020 (1952.02 mm), and minimum during 2018 (225.66 mm) • The analysis shows that the variability of GWS over the study domain is high in the western part while low in the eastern part within the study period. • It was perceived that climate variability directly impacts the vulnerability of the livestock industry in Southeastern Ethiopia. Moreover, these results can help local climate-resilient development planning and enhance coordination with other institutions to access and manage climate finance. This research also discusses climate-resilient agriculture and development practices for sustainable planning and management purposes. The recommendations also included for future development of the climate-resilient agriculture (CRA) system. 31 10. References Abebe Tadege 2007. Climate change National Adaptation Programme of Action (NAPA) of Ethiopia. Global Environmental Facility(GEF), 2, 96. Adem A. & Amsalu, A. 2012. Assessment of climate change-induced hazards, impacts, and responses in the southern lowlands of Ethiopia. In Ethiopian Journal of Development Research, 34, 1. Admassie A. Adenew, B. & Tadege, A. 2008. Perceptions of stakeholders on climate change and adaptation strategies in Ethiopia. IFPRI Research Brief, 15(6), 2. Awange, J. L., Gebremichael, M., Forootan, E., Wakbulcho, G., Anyah, R., Ferreira, V. G., & Alemayehu, T. 2014. Characterization of Ethiopian mega hydrogeological regimes using GRACE, TRMM and GLDAS datasets. Advances in Water Resources, 74, 64–78. Barlow P. M. Alley W. M. & Myers D. N. 2004. Hydrologic Aspects of Water Sustainability and Their Relation to a National Assessment of Water Availability and Use. Water Resources Update, 127, 76–86. Bosch, J. M., & Hewlett, J. D. 1982. A review of catchment experiments to determine the effect of vegetation changes on water yield and evapotranspiration. Journal of Hydrology, 55(1–4), 3–23. Chen J. L. Wilson C. R. & Tapley B. D. 2010. The 2009 exceptional Amazon flood and interannual terrestrial water storage change observed by GRACE. Water Resources Research, 46(12). Conway, D. 2000. The climate and hydrology of the Upper Blue Nile river. Geographical Journal, 166(1), 49–62. Conway, D., & Schipper, E. L. F. 2011. Adaptation to climate change in Africa: Challenges and opportunities identified from Ethiopia. Global Environmental Change, 21(1), 227–237. Costa, M. H., Botta, A., & Cardille, J. A. 2003. Effects of large-scale changes in land cover on the discharge of the Tocantins River, Southeastern Amazonia. Journal of Hydrology, 283(1–4), 206–217. Dong, L., Xiong, L., Lall, U., & Wang, J. 2015. The effects of land use change and precipitation change on direct runoff in Wei River watershed, China. Water Science and Technology, 71(2), 289–295. Dube, T., Moyo, P., Ncube, M., & Nyathi, D. 2016. The Impact of Climate Change on Agro-Ecological Based Livelihoods in Africa: A Review. Journal of Sustainable Development, 9(1), 256. Dwivedi, R. S., Sreenivas, K., & Ramana, K. V. 2005. Land-use/land-cover change analysis in part of Ethiopia using Landsat Thematic Mapper data. International Journal of Remote Sensing, 26(7), 1285–1287. Famiglietti, J. S., & Rodell, M. 2013. Water in the balance. Science, 340(6138), 1300–1301. Fang, X., Ren, L., Li, Q., Zhu, Q., Shi, P., & Zhu, Y. 2013. Hydrologic Response to Land Use and Land Cover Changes within the Context of Catchment-Scale Spatial Information. Journal of Hydrologic Engineering, 18(11), 1539–1548. 32 Forootan, E., Rietbroek, R., Kusche, J., Sharifi, M. A., Awange, J. L., Schmidt, M., Omondi, P., & Famiglietti, J. 2014. Separation of large scale water storage patterns over Iran using GRACE, altimetry and hydrological data. Remote Sensing of Environment, 140, 580–595. Garg, V., Nikam, B. R., Thakur, P. K., Aggarwal, S. P., Gupta, P. K., & Srivastav, S. K. 2019. Human-induced land use land cover change and its impact on hydrology. HydroResearch, 1, 48–56. Gashaw, T., Tulu, T., Argaw, M., & Worqlul, A. W. 2018. Modeling the hydrological impacts of land use/land cover changes in the Andassa watershed, Blue Nile Basin, Ethiopia. Science of the Total Environment, 619–620, 1394–1408. Guo, H., Bao, A., Ndayisaba, F., Liu, T., Jiapaer, G., El-Tantawi, A. M., & De Maeyer, P. 2018. Space-time characterization of drought events and their impacts on vegetation in Central Asia. Journal of Hydrology, 564, 1165–1178. Guo, H., Hu, Q., & Jiang, T. 2008. Annual and seasonal streamflow responses to climate and land-cover changes in the Poyang Lake basin, China. Journal of Hydrology, 355(1–4), 106–122. Hao Z. AghaKouchak, A., Nakhjiri, N., & Farahmand, A. 2014. Global integrated drought monitoring and prediction system. Scientific Data, 1(1), 1–10. Jaramillo, F., & Destouni, G. 2015. Local flow regulation and irrigation raise global human water consumption and footprint. Science, 350(6265), 1248–1251. Kenea, T. T., Kusche, J., Kebede, S., & Güntner, A. 2020. Forecasting terrestrial water storage for drought management in Ethiopia. Hydrological Sciences Journal, 65(13), 2210–2223. Legesse, B., Ayele, Y., & Bewket, W. 2013. Smallholder Farmers’ Perceptions and Adaptation to Climate Variability and Climate Change in Doba District, West Hararghe, Ethiopia. Asian Journal of Empirical Research, 3(3), 251–265. Li, G., Zhang, F., Jing, Y., Liu, Y., & Sun, G. 2017. Response of evapotranspiration to changes in land use and land cover and climate in China during 2001–2013. Science of the Total Environment, 596–597, 256–265. Li, T., Zhang, Q., Zhao, Y., & Gao, Y. 2019. Detection of groundwater storage variability based on GRACE and CABLE model in the Murray-Darling Basin. E3S Web of Conferences, 131(01067). Li, Z., Liu, W. zhao, Zhang, X. chang, & Zheng, F. li. 2009. Impacts of land use change and climate variability on hydrology in an agricultural catchment on the Loess Plateau of China. Journal of Hydrology, 377(1–2), 35–42. Ma, X., Xu, J., Luo, Y., Prasad Aggarwal, S., & Li, J. 2009. Response of hydrological processes to land-cover and climate changes in Kejie watershed, south-west China. Hydrological Processes: An International Journal, 23, 1179– 1191. Maiolo, M., & Pantusa, D. (2019). Sustainable water management index, SWaM_Index. Cogent Engineering, 6(1), 1603817. Maliehe, M., & Mulungu, D. M. M. 2017. Assessment of water availability for competing uses using SWAT and WEAP in South Phuthiatsana catchment, Lesotho. Physics and Chemistry of the Earth, 100, 305–316. 33 Masson-Delmotte, V., Zhai, P., Pörtner, H.-O., Debra Roberts, J., Skea, I., Shukla, P. R., & Pirani, A. (2018). Global warming of 1.5°C. An IPCC Special Report on the Impacts of Global Warming, 1(5). Mendes, J., & Maia, R. 2016. Hydrologic Modelling Calibration for Operational Flood Forecasting. Water Resources Management, 30(15), 5671–5685. Mo, X., Wu, J. J., Wang, Q., & Zhou, H. 2016. Variations in water storage in China over recent decades from GRACE observations and GLDAS. Natural Hazards and Earth System Sciences, 16(2), 469–482. Muema, E., Mburu, J., Coulibaly, J., & Mutune, J. 2018. Determinants of access and utilisation of seasonal climate information services among smallholder farmers in Makueni County, Kenya. Heliyon, 4(11), e00889. Oakes, T. 2009. Climate Change 2007: Impacts, Adaptation and Vulnerability The. In International Encyclopedia of Human Geography. Padowski, J. C., & Jawitz, J. W. 2012. Water availability and vulnerability of 225 large cities in the United States. Water Resources Research, 48(12), 1–16. Prakash, S., Gairola, R. M., Papa, F., & Mitra, A. K. 2014. An assessment of terrestrial water storage, rainfall and river discharge over Northern India from satellite data. Current Science, 107(9), 1582–1586. Ramillien, G., Frappart, F., Güntner, A., Ngo-Duc, T., Cazenave, A., & Laval, K. 2006. Time variations of the regional evapotranspiration rate from Gravity Recovery and Climate Experiment (GRACE) satellite gravimetry. Water Resources Research, 42(10). Rodell, M., Chen, J., Kato, H., Famiglietti, J. S., Nigro, J., & Wilson, C. R. 2007. Estimating groundwater storage changes in the Mississippi River basin (USA) using GRACE. Hydrogeology Journal, 15(1), 159–166. Rodell, M., Houser, P. R., Jambor, U., Gottschalck, J., Mitchell, K., Meng, C. J., Arsenault, K., Cosgrove, B., Radakovich, J., Bosilovich, M., Entin, J. K., Walker, J. P., Lohmann, D., & Toll, D. 2004. The Global Land Data Assimilation System. Bulletin of the American Meteorological Society, 85(3), 381–394. Rovin, K., Hardee, K., & Kidanu, A. 2013. Linking population, fertility, and family planning with adaptation to climate change: perspectives from Ethiopia. African Journal of Reproductive Health, 17(3), 15–29. Saber, M., Kantoush, S. A., & Sumi, T. 2020. Assessment of spatiotemporal variability of water storage in Arabian countries using global datasets: implications for water resources management. Urban Water Journal, 17(5), 416–430. Seyoum, W. M. 2018. Characterizing water storage trends and regional climate influence using GRACE observation and satellite altimetry data in the Upper Blue Nile River Basin. Journal of Hydrology, 566, 274–284. Shamsudduha, M., Taylor, R. G., Jones, D., Longuevergne, L., Owor, M., & Tindimugaya, C. 2017. Recent changes in terrestrial water storage in the Upper Nile Basin: An evaluation of commonly used gridded GRACE products. Hydrology and Earth System Sciences, 21(9), 4533–4549. 34 Stige, L. C., Stave, J., Chan, K. S., Ciannelli, L., Pettorelli, N., Glantz, M., Herren, H. R., & Stenseth, N. C. 2006. The effect of climate variation on agro-pastoral production in Africa. Proceedings of the National Academy of Sciences of the United States of America, 103(9), 3049–3053. Sultan, B., & Gaetani, M. 2016. Agriculture in West Africa in the twenty-first century: Climate change and impacts scenarios, and potential for adaptation. Frontiers in Plant Science, 7, 1262. Syed, T. H., Famiglietti, J. S., Rodell, M., Chen, J., & Wilson, C. R. 2008. Analysis of terrestrial water storage changes from GRACE and GLDAS. Water Resources Research, 44(2). Sylla, M. B., Pal, J. S., Faye, A., Dimobe, K., & Kunstmann, H. 2018. Climate change to severely impact West African basin scale irrigation in 2 °C and 1.5 °C global warming scenarios. Scientific Reports, 8(1), 1–9. Teklay, A., Dile, Y. T., Setegn, S. G., Demissie, S. S., & Asfaw, D. H. 2019. Evaluation of static and dynamic land use data for watershed hydrologic process simulation: A case study in Gummara watershed, Ethiopia. Catena, 172, 65–75. Tesfahunegn, G. B., Mekonen, K., & Tekle, A. 2016. Farmers’ perception on causes, indicators and determinants of climate change in northern Ethiopia: Implication for developing adaptation strategies. Applied Geography, 73, 1–12. Thapa, P. 2021. The Relationship between Land Use and Climate Change: A Case Study of Nepal. The Nature, Causes, Effects and Mitigation of Climate Change on the Environment. Thornton, P. K., Jones, P. G., Alagarswamy, G., Andresen, J., & Herrero, M. 2010. Adapting to climate change: Agricultural system and household impacts in East Africa. Agricultural Systems, 103(2), 73–82. Wahr, J., Molenaar, M., & Bryan, F. 1998. Time variability of the Earth’s gravity field’ Hydrological and oceanic effects and their possible detection using GRACE. Journal of Geophysical Research: Solid Earth, 103(B12), 30205– 30229. Wang, X., De Linage, C., Famiglietti, J., & Zender, C. S. 2011. Gravity Recovery and Climate Experiment (GRACE) detection of water storage changes in the Three Gorges Reservoir of China and comparison with in situ measurements. Water Resources Research, 47(12). Winkler, K., Gessner, U., & Hochschild, V. 2017. Identifying droughts affecting agriculture in Africa based on remote sensing time series between 2000-2016: Rainfall anomalies and vegetation condition in the context of ENSO. Remote Sensing, 9(8), 831. Woldesenbet, T. A., Elagib, N. A., Ribbe, L., & Heinrich, J. 2017. Hydrological responses to land use/cover changes in the source region of the Upper Blue Nile Basin, Ethiopia. Science of the Total Environment, 575, 724–741. Worku, S., Derbie, A., Sinishaw, M. A., Adem, Y., & Biadglegne, F. 2017. Prevalence of Bacteriuria and Antimicrobial Susceptibility Patterns among Diabetic and Nondiabetic Patients Attending at Debre Tabor Hospital, Northwest Ethiopia. International Journal of Microbiology. 35 Yang, X., Ren, L., Singh, V. P., Liu, X., Yuan, F., Jiang, S., & Yong, B. 2012. Impacts of land use and land cover changes on evapotranspiration and runoff at Shalamulun River watershed, China. Hydrology Research, 43(1–2), 23–37. Yao, J., Hu, W., Chen, Y., Huo, W., Zhao, Y., Mao, W., & Yang, Q. 2019. Hydro-climatic changes and their impacts on vegetation in Xinjiang, Central Asia. Science of the Total Environment, 660, 724–732. Yin, J., He, F., Jiu Xiong, Y., & Yu Qiu, G. 2017. Effects of land use/land cover and climate changes on surface runoff in a semi-humid and semi-arid transition zone in northwest China. Hydrology and Earth System Sciences, 21(1), 183–196. Zhang, L., Dawes, W. R., & Walker, G. R. 2001. Response of mean annual evapotranspiration to vegetation changes at catchment scale. Water Resources Research, 37(3), 701–708. Zhang, Y., Guan, D., Jin, C., Wang, A., Wu, J., & Yuan, F. 2014. Impacts of climate change and land use change on runoff of forest catchment in northeast China. Hydrological Processes, 28(2), 186–196. 36