Comparing the performance of high‐resolution global precipitation products across topographic and climatic gradients of Central Asia

Accurate and reliable precipitation data with high spatial and temporal resolution are essential in studying climate variability, water resources management, and hydrological forecasting. A range of global precipitation data are available to this end, but how well these capture actual precipitation remains unknown, particularly for mountain regions where ground stations are sparse. We examined the performance of three global high‐resolution precipitation products for capturing precipitation over Central Asia, a hotspot of climate change, where reliable precipitation data are particularly scarce. Specifically, we evaluated MSWEP, CHIRPS, and GSMAP against independent gauging stations for the period 1985–2015. Our results show that MSWEP and CHIRPS outperformed GSMAP for wetter periods (i.e., winter and spring) and wetter locations (150–600 mm·year−1), lowlands, and mid‐altitudes (0–3,000 m), and regions dominated by winter and spring precipitation. MSWEP performed best in representing temporal precipitation dynamics and CHIRPS excelled in capturing the volume and distribution of precipitation. All precipitation products poorly estimated precipitation at higher elevations (>3,000 m), in drier areas (<150 mm), and in regions characterized by summer precipitation. All products accurately detected dry spells, but their performance decreased for wet spells with increasing precipitation intensity. In sum, we find that CHIRPS and MSWEP provide the most reliable high‐resolution precipitation estimates for Central Asia. However, the high spatial and temporal heterogeneity of the performance call for a careful selection of a suitable product for local applications considering the prevailing precipitation dynamics, climatic, and topographic conditions.We present the first quantitative evaluation of global high‐resolution (below 12 km) precipitation products against independent ground observations over Central Asia. Our results show that MSWEP was best at representing temporal precipitation dynamics, and CHIRPS was most prominent in representing the volume and distribution of precipitation. This is especially the case of wet seasons, altitudes below 3,000 m, and regions dominated by spring and winter precipitation. Our analysis provides key insights on the precipitation products' suitability for local hydrological applications. imageLeibniz‐Institut für Agrarentwicklung in TransformationsökonomienVolkswagen Foundation http://dx.doi.org/10.13039/501100001663Peer Reviewe

Spatial-temporal trends of rainfall, maximum and minimum temperatures over west Africa

Open Access Article; Published online: 25 May 2020This article investigates the magnitude and significance of spatial-temporal trends of 37 years' time series of the gridded data for rainfall, maximum (Tmax) and minimum (Tmin) temperature for West Africa. A modified Mann-Kendall test and Theil-Sen's slope estimator were utilized to test the significance and the magnitude of trends, respectively. The magnitude of significant trends for three variables between six agroecological zones (AEZs) was compared. Gridded climate data represented gauge data with high accuracy and, therefore, can reliably complement the sparse observation network in West Africa. The three variables showed significant positive and negative trends of varying magnitude and spatial extent. June to September rainfall showed a positive increase (0.1-5 mm/month/year) that mostly occurred north of 11° latitude. October rainfall showed a positive trend across the region, but the magnitude was higher south of the same latitude. A widespread significant warming trend was observed across all AEZs and months. However, a localized cooling in August and September over the Sahel and Sudan Savanna was an exception. The cooling over the two AEZs coincided with a positive trend of rainfall. The zonal analysis revealed that the magnitude of the positive trend of June, September, and October rain increased following a North-South gradient from the Sahel to humid forest AEZs. Results provide spatial evidence of climate change in a limited data environment to guide the targeting of appropriate adaptation measures. The information generated from this article helps the design of early warning systems against droughts and floods

Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling

Abstract. We undertook a comprehensive evaluation of 22 gridded (quasi-)global (sub-)daily precipitation (P) datasets for the period 2000–2016. Thirteen non-gauge-corrected P datasets were evaluated using daily P gauge observations from 76 086 gauges worldwide. Another nine gauge-corrected datasets were evaluated using hydrological modeling, by calibrating the HBV conceptual model against streamflow records for each of 9053 small to medium-sized ( <  50 000 km2) catchments worldwide, and comparing the resulting performance. Marked differences in spatio-temporal patterns and accuracy were found among the datasets. Among the uncorrected P datasets, the satellite- and reanalysis-based MSWEP-ng V1.2 and V2.0 datasets generally showed the best temporal correlations with the gauge observations, followed by the reanalyses (ERA-Interim, JRA-55, and NCEP-CFSR) and the satellite- and reanalysis-based CHIRP V2.0 dataset, the estimates based primarily on passive microwave remote sensing of rainfall (CMORPH V1.0, GSMaP V5/6, and TMPA 3B42RT V7) or near-surface soil moisture (SM2RAIN-ASCAT), and finally, estimates based primarily on thermal infrared imagery (GridSat V1.0, PERSIANN, and PERSIANN-CCS). Two of the three reanalyses (ERA-Interim and JRA-55) unexpectedly obtained lower trend errors than the satellite datasets. Among the corrected P datasets, the ones directly incorporating daily gauge data (CPC Unified, and MSWEP V1.2 and V2.0) generally provided the best calibration scores, although the good performance of the fully gauge-based CPC Unified is unlikely to translate to sparsely or ungauged regions. Next best results were obtained with P estimates directly incorporating temporally coarser gauge data (CHIRPS V2.0, GPCP-1DD V1.2, TMPA 3B42 V7, and WFDEI-CRU), which in turn outperformed the one indirectly incorporating gauge data through another multi-source dataset (PERSIANN-CDR V1R1). Our results highlight large differences in estimation accuracy, and hence the importance of P dataset selection in both research and operational applications. The good performance of MSWEP emphasizes that careful data merging can exploit the complementary strengths of gauge-, satellite-, and reanalysis-based P estimates

Enhanced flood mapping using synthetic aperture radar (SAR) images, hydraulic modelling, and social media: A case study of Hurricane Harvey (Houston, TX)

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The CAMELS-CL dataset: catchment attributes and meteorology for large sample studies – Chile dataset

We introduce the first catchment dataset for large sample studies in Chile. This dataset includes 516 catchments; it covers particularly wide latitude (17.8 to 55.0∘ S) and elevation (0 to 6993 m a.s.l.) ranges, and it relies on multiple data sources (including ground data, remote-sensed products and reanalyses) to characterise the hydroclimatic conditions and landscape of a region where in situ measurements are scarce. For each catchment, the dataset provides boundaries, daily streamflow records and basin-averaged daily time series of precipitation (from one national and three global datasets), maximum, minimum and mean temperatures, potential evapotranspiration (PET; from two datasets), and snow water equivalent. We calculated hydro-climatological indices using these time series, and leveraged diverse data sources to extract topographic, geological and land cover features. Relying on publicly available reservoirs and water rights data for the country, we estimated the degree of anthropic intervention within the catchments. To facilitate the use of this dataset and promote common standards in large sample studies, we computed most catchment attributes introduced by Addor et al. (2017) in their Catchment Attributes and MEteorology for Large-sample Studies (CAMELS) dataset, and added several others. We used the dataset presented here (named CAMELS-CL) to characterise regional variations in hydroclimatic conditions over Chile and to explore how basin behaviour is influenced by catchment attributes and water extractions. Further, CAMELS-CL enabled us to analyse biases and uncertainties in basin-wide precipitation and PET. The characterisation of catchment water balances revealed large discrepancies between precipitation products in arid regions and a systematic precipitation underestimation in headwater mountain catchments (high elevations and steep slopes) over humid regions. We evaluated PET products based on ground data and found a fairly good performance of both products in humid regions (r>0.91) and lower correlation (r<0.76) in hyper-arid regions. Further, the satellite-based PET showed a consistent overestimation of observation-based PET. Finally, we explored local anomalies in catchment response by analysing the relationship between hydrological signatures and an attribute characterising the level of anthropic interventions. We showed that larger anthropic interventions are correlated with lower than normal annual flows, runoff ratios, elasticity of runoff with respect to precipitation, and flashiness of runoff, especially in arid catchments. CAMELS-CL provides unprecedented information on catchments in a region largely underrepresented in large sample studies. This effort is part of an international initiative to create multi-national large sample datasets freely available for the community. CAMELS-CL can be visualised from http://camels.cr2.cl and downloaded from https://doi.pangaea.de/10.1594/PANGAEA.894885

Global high-resolution drought indices for 1981-2022

Droughts are among the most complex and devastating natural hazards globally. High-resolution datasets of drought metrics are essential for monitoring and quantifying the severity, duration, frequency and spatial extent of droughts at regional and particularly local scales. However, current global drought indices are available only at a coarser spatial resolution (>50 km). To fill this gap, we developed five high-resolution (5 km) gridded drought records based on the Standardized Precipitation Evaporation Index (SPEI) covering the period 1981–2022. These multi-scale (1–48 months) SPEI indices are computed based on monthly precipitation (P) from the Climate Hazards group InfraRed Precipitation with Station data (CHIRPS, version 2) and Multi-Source Weighted-Ensemble Precipitation (MSWEP, version 2.8) and potential evapotranspiration (PET) from the Global Land Evaporation Amsterdam Model (GLEAM, version 3.7a) and Bristol Potential Evapotranspiration (hPET). We generated four SPEI records based on all possible combinations of P and PET datasets: CHIRPS-GLEAM, CHIRPS-hPET, MSWEP-GLEAM, and MSWEP-hPET. These drought records were evaluated globally and exhibited excellent agreement with observation-based estimates of SPEI, root zone soil moisture, and vegetation health indices. The newly developed high-resolution datasets provide more detailed local information and be used to assess drought severity for particular periods and regions and to determine global, regional, and local trends, thereby supporting the development of site-specific adaptation measures. These datasets are publicly available at the Centre for Environmental Data Analysis (CEDA): https://dx.doi.org/10.5285/ac43da11867243a1bb414e1637802dec (Gebrechorkos et al., 2023)

Use and Improvement of Remote Sensing and Geospatial Technologies in Support of Crop Area and Yield Estimations in the West African Sahel

In arid and semi-arid West Africa, agricultural production and regional food security depend largely on small-scale subsistence farming and rainfed crops, both of which are vulnerable to climate variability and drought. Efforts made to improve crop monitoring and our ability to estimate crop production (areas planted and yield estimations by crop type) in the major agricultural zones of the region are critical paths for minimizing climate risks and to support food security planning. The main objective of this dissertation research was to contribute to these efforts using remote sensing technologies. In this regard, the first analysis documented the low reliability of existing land cover products for cropland area estimation (Chapter 2). Then two satellite remote sensing-based datasets were developed that 1) accurately map cropland areas in the five countries of Sahelian West Africa (Senegal, Mauritania, Mali, Burkina Faso and Niger; Chapter 3), and 2) focus on the country of Mali to identify the location and prevalence of the major subsistence crops (millet, sorghum, maize and non-irrigated rice; Chapter 4). The regional cropland area product is distributed as the West African Sahel Cropland area at 30 m (WASC30). The development of the new dataset involved high density training data (380,000 samples) developed by USGS in collaboration with CILSS for training about 200 locally optimized random forest (RF) classifiers using Landsat 8 surface reflectances and vegetation indices and the Google Earth Engine platform. WASC30 greatly improves earlier estimates through inclusion of cropland information for both rainfed and irrigated areas mapped with a class-specific accuracy of 79% across the West Africa Sahel. Used as a mask in crop monitoring systems, the new cropland area data could bring critical insights by reducing uncertainties in xv identification of croplands as crop growth condition metrics are extracted. WASC30 allowed us to derive detailed statistics on cultivated areas in the Sahel, at country and agroclimatic scales. Intensive agricultural zones were highlighted as well. The second dataset, mapping crop types for the country of Mali, is meant to separate signals of different crop types for improved crop yield estimation. The crop type map was used to derive detailed agricultural statistics (e.g. acreage by crop types, spatial distribution) at finer administrative scales than has previously been possible. The crop fraction information by crop type extracted from the map, gives additional details on farmers preferences by regions, and the natural adaptability of different crop types. The final analysis of this dissertation explores the use of ensemble machine learning techniques to predict maize yield in Mali (Chapter 5). Climate data (precipitation and temperature), and vegetation indices (Normalized Difference Vegetation Index, NDVI, the Enhanced Vegetation Index, EVI, and the Normalized Difference Water Index, NDWI) are used as predictors, while actual yields collected in 2017 by the Malian Ministry of Agriculture are the reference data. Random forest presented better predictive performance as compared to boosted regression trees (BRT). Results showed that climate variables have more predictive power for maize yield compared to vegetation indices. Among vegetation indices, the NDWI appeared to be the most influential predictor, maybe because of water requirement of maize and the sensitivity of this index to water in semi-arid regions. Tested with two different independent datasets, one constituted by 20% of the reference information, and another including observed yields for year 2018 (a one-year-left analysis), maize yield predictions were promising for year 2017 (RMSE = 362 kg/ha), but showed higher error for 2018 (RMSE = 707 kg/ha). That is, the fitted model may not capture accurately year to year variabilities in predicted maize yield. In this analysis, predictions were limited to field samples (~600 fields) across the country of Mali. It would be valuable in the future to predict maize yield for each pixel of the new developed crop type map. That will lead to a detailed spatial analysis of maize yield, allowing identification of low yielding regions for targeted interventions which could improve food security. Keywords: Agricultural identification of croplands as crop growth condition metrics are extracted. WASC30 allowed us to derive detailed statistics on cultivated areas in the Sahel, at country and agroclimatic scales. Intensive agricultural zones were highlighted as well. The second dataset, mapping crop types for the country of Mali, is meant to separate signals of different crop types for improved crop yield estimation. The crop type map was used to derive detailed agricultural statistics (e.g. acreage by crop types, spatial distribution) at finer administrative scales than has previously been possible. The crop fraction information by crop type extracted from the map, gives additional details on farmers preferences by regions, and the natural adaptability of different crop types. The final analysis of this dissertation explores the use of ensemble machine learning techniques to predict maize yield in Mali (Chapter 5). Climate data (precipitation and temperature), and vegetation indices (Normalized Difference Vegetation Index, NDVI, the Enhanced Vegetation Index, EVI, and the Normalized Difference Water Index, NDWI) are used as predictors, while actual yields collected in 2017 by the Malian Ministry of Agriculture are the reference data. Random forest presented better predictive performance as compared to boosted regression trees (BRT). Results showed that climate variables have more predictive power for maize yield compared to vegetation indices. Among vegetation indices, the NDWI appeared to be the most influential predictor, maybe because of water requirement of maize and the sensitivity of this index to water in semi-arid regions. Tested with two different independent datasets, one constituted by 20% of the reference information, and another including observed yields for year 2018 (a one-year-left analysis), maize yield predictions were promising for year 2017 (RMSE = 362 kg/ha), but showed higher error for 2018 (RMSE = 707 kg/ha). That is, the fitted model may not capture accurately year to year variabilities in predicted maize yield. In this analysis, predictions were limited to field samples (~600 fields) across the country of Mali. It would be valuable in the future to predict maize yield for each pixel of the new developed crop type map. That will lead to a detailed spatial analysis of maize yield, allowing identification of low yielding regions for targeted interventions which could improve food security

A spatio-temporal analysis of trends in rainfall from long term satellite rainfall products in the Sudano Sahelian zone of Nigeria

Rainfall and its variability drive the rural economies across the Sudano-Sahelian zone of northern Nigeria, where drought strategies largely determine crop yields. The increasing scarcity of rain gauges in West Africa generally limits assessments of the degree and spatial extent of hardship arising from rainfall deficiency. However, the improved availability and robustness of satellite-based rainfall products since the early 1980s, offers an alternative source of rainfall data which is spatially, and often temporally, more complete than rain gauges. This research evaluates four satellite-based rainfall products for their ability to represent both long term rainfall trends such as recovery from decadal droughts, and trends in seasonal rainfall variables relevant to crop yield prediction. The Climate Hazards group Infrared Precipitation with Stations (CHIRPS) rainfall product at 5 km resolution, was observed to be consistently most representative of ground station rainfall across northern Nigeria over a 35-year period 1981–2015, followed by TARCAT. CHIRPS was found to give a good overall prediction of rainfall amounts at dekadal, monthly and seasonal time scales, and was therefore used in the study to represent the typical performance of satellite rainfall datasets. The CHIRPS-observed increase in growing season length since the 1970s and 80s drought decades, was accompanied by significant rainfall increases in the later part of the growing season, especially marked in northern and northeastern states. This is especially important for the main subsistence crops sorghum and millet as the risk of late drought impedes swelling of the grain, affecting dry weight production. The CHIRPS data also indicate a significant decrease in dry spells in the northwest and southern parts of the study area, which would have favourable outcomes for crop production in the densely populated rural hinterlands of the cities of Sokoto, Jos and Abuja. In view of the continued intra-and inter-annual rainfall variability across northern Nigeria, and amid rapid rural population growth recently, a return to the rainfall levels of the drought decades, would require informed response. The study suggests that satellite rainfall estimates can offer such information, especially since we observed high spatial variability in rainfall distributions and trends

A Comprehensive Evaluation of Latest GPM IMERG V06 Early, Late and Final Precipitation Products across China

This study evaluated the performance of the early, late and final runs of IMERG version 06 precipitation products at various spatial and temporal scales in China from 2008 to 2017, against observations from 696 rain gauges. The results suggest that the three IMERG products can well reproduce the spatial patterns of precipitation, but exhibit a gradual decrease in the accuracy from the southeast to the northwest of China. Overall, the three runs show better performances in the eastern humid basins than the western arid basins. Compared to the early and late runs, the final run shows an improvement in the performance of precipitation estimation in terms of correlation coefficient, Kling–Gupta Efficiency and root mean square error at both daily and monthly scales. The three runs show similar daily precipitation detection capability over China. The biases of the three runs show a significantly positive (p < 0.01) correlation with elevation, with higher accuracy observed with an increase in elevation. However, the categorical metrics exhibit low levels of dependency on elevation, except for the probability of detection. Over China and major river basins, the three products underestimate the frequency of no/tiny rain events (P < 0.1 mm/day) but overestimate the frequency of light rain events (0.1 ≤ P < 10 mm/day). The three products converge with ground-based observation with regard to the frequency of rainstorm (P ≥ 50 mm/day) in the southern part of China. The revealed uncertainties associated with the IMERG products suggests that sustaining efforts are needed to improve their retrieval algorithms in the future